Mobile Robot Global Localization using an Evolutionary MAP Filter

A new algorithm based on evolutionary computation concepts is presented in this paper. This algorithm is a non linear evolutive filter known as the Evolutive Localization Filter (ELF) which is able to solve the global localization problem in a robust and efficient way. The proposed algorithm searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information up to date. The population evolves by using the log-likelihood of each candidate pose according to the observation and the motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The algorithm has been tested on a mobile robot equipped with a laser range finder to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.     

A 2D chaotic path planning for mobile robots accomplishing boundary surveillance missions in adversarial conditions

The path-planning algorithm represents a crucial issue for every autonomous mobile robot. In normal circumstances a patrol robot will compute an optimal path to ensure its task accomplishment, but in adversarial conditions the problem is getting more complicated. Here, the robot’s trajectory needs to be altered into a misleading and unpredictable path to cope with potential opponents. Chaotic systems provide the needed framework for obtaining unpredictable motion in all of the three basic robot surveillance missions: area, points of interests and boundary monitoring. Proficient approaches have been provided for the first two surveillance tasks, but for boundary patrol missions no method has been reported yet. This paper addresses the mentioned research gap by proposing an efficient method, based on chaotic dynamic of the Hénon system, to ensure unpredictable boundary patrol on any shape of chosen closed contour.     

Energy-Optimal Multi-Goal Motion Planning for Planar Robot Manipulators

In this work, the energy-optimal motion planning problem for planar robot manipulators with two revolute joints is studied, in which the end-effector of the robot manipulator is constrained to pass through a set of waypoints, whose sequence is not predefined. This multi-goal motion planning problem has been solved as a mixed-integer optimal control problem in which, given the dynamic model of the robot manipulator, the initial and final configurations of the robot, and a set of waypoints inside the workspace of the manipulator, one has to find the control inputs, the sequence of waypoints with the corresponding passage times, and the resulting trajectory of the robot that minimizes the energy consumption during the motion. The presence of the waypoint constraints makes this optimal control problem particularly difficult to solve. The mixed-integer optimal control problem has been converted into a mixed-integer nonlinear programming problem first making the unknown passage times through the waypoints part of the state, then introducing binary variables to enforce the constraint of passing once through each waypoint, and finally applying a fifth-degree Gauss–Lobatto direct collocation method to tackle the dynamic constraints. High-degree interpolation polynomials allow the number of variables of the problem to be reduced for a given numerical precision. The resulting mixed-integer nonlinear programming problem has been solved using a nonlinear programming-based branch-and-bound algorithm specifically tailored to the problem. The results of the numerical experiments have shown the effectiveness of the approach.     

Design an interactive virtual physics environment with uncertainties control to support motion tracking

The development of robot or character motion tracking algorithms is inherently a challenging task. This is more than ever true when the latest trends in motion tracking are considered. Some researchers can deal with kinematic and dynamic constraints induced by the mechanical structure. Another class of researchers fulfills various types of optimality conditions, yet others include means of dealing with uncertainties about the robot or character and its environment. In order to deal with the complexity of developing motion tracking algorithms, it is proposed in this paper to design an interactive virtual physics environment with uncertainties for motion tracking based on sliding mode control. The advantages of doing so are outlined and a virtual environment presented which is well suited to support motion tracking development. The environment makes full use of multi-body system dynamics and a robust sliding mode controller independent of model as simulation kernel. So the environment is capable of simulating setups which fulfill the requirements posed by state-of-the-art motion tracking algorithm development. The demonstration results verified the validity of the environment.     

Dynamic Modeling and Force Control of a Redundant Robot for Polishing Applications

For many robotic applications with tasks such as cutting, assembly or polishing, it is necessary to get in contact with the surrounding. In this paper a redundant robot with seven degrees of freedom in a metal polishing task is considered. For simulation as well as for the controller design a dynamic model of the robot and a contact model are required. The equations of motion of the robot are calculated with the Projection Equation in subsystem representation and the contact model contains linear tool elasticities and work piece elasticities. In the case of a polishing task, a constant contact force during the process is required even if the robot moves along a trajectory. Thus some degrees of freedom of the robot tool center point have to be position controlled while the other ones have to be force controlled. The redundant robot offers the possibility to avoid singular positions or to maximize the available end-effector forces within the inverse kinematics and is therefore best suited for polishing large objects. The actual process forces are measured with a six axis force-torque-sensor mounted at the tool center point. These forces are used in a parallel force/position control law to achieve the desired behavior. Results from measurements of a test arrangement are presented. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Learning Robot Force/Position Control for Repetitive High Speed Applications with Unknown Non–Linear Contact Stiffness

This paper proposes a learning robot force/position control for high speed force trajectory following. Following high speed force trajectories in different repetitve robotic applications is a challenging field in robot force control. If the end–effector should provide a contact force while following a position trajectory in the non–force controlled direction a parallel force / position control is suitable. However, when it comes to high speed tasks this force control method reaches its limit. The problem can be solved by using an iterative learning control method in combination with the parallel force/position control. In this paper the learning force control method is introduced and experimental results are presented. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Hybrid control for tracking environmental level sets by nonholonomic robots in maze-like environments

A non-holonomic constant-speed robot travels in an unknown maze-like environment cluttered with complex obstacles. Through the obstacle-free part of the plane, the robot should autonomously arrive at the isoline where an unknown scalar field assumes a given value. Afterwards, it should track the obstacle-free part of the isoline. The robot has access only to the field value at the current location and the distance from this location to the obstacles. We present a hybrid nonlinear navigation law that solves this mission. The law does not use estimation of the field gradient and is non-demanding with respect to both computation and motion. The non-local convergence of the proposed algorithm is rigorously justified and confirmed by computer simulation tests.     

“货到人”拣选系统机器人任务分配的鲁棒双层规划模型

在电商“货到人”拣选系统中,如何调度系统中的机器人并对任务进行合理地分配决定着整个系统的运行效率与成本。分析“货到人”拣选系统作业流程,建立机器人数量配置、机器人调度与机器人任务分配的双层规划模型。上层模型以批量订单完成总成本最小为目标函数,以机器人调度为决策变量,构建整数规划模型;下层模型以机器人完成所有任务的平均空闲率最小为目标函数,以任务分配为决策变量,考虑机器人在完成任务过程中由于调度、避障、路径规划等导致的行走距离不确定因素,构建鲁棒优化模型。上层的调度结果制约了下层的最小平均空闲率,下层的任务分配结果影响上层的最小成本,上下层结果共同决定机器人配置决策。利用遗传算法求解模型,通过实例仿真验证了模型的有效性。     

Computer programs for non‐linear programming

In this paper we consider the problem of maximizing a non‐linear or linear objective function subject to non‐linear and/or linear constraints. The approach used is an adaptive random search with some non‐random searches built‐in. The algorithm begins with a given point which is replaced by another point if the latter satisfies each of the constraints and results in a bigger functional value. The process of moving from one point to a better point is repeated many times. The value of each of the coordinates of the next point is determined by one of several ways; for example, a coordinate is sometimes forced to have the same value as the value of the corresponding coordinate of the current feasible point. In this algorithm, a candidate point receives no further computational considerations as soon as it is found to be unfeasible; this makes the algorithm general. Computer programs illustrating the details of the new algorithm are given and computational results of two numerical test problems from the literature are presented. Optimality was reached in each of these two problems.     

拟自适应分类随机森林算法

本文给出了集成学习模型可以收敛的集成学习算法,拟自适应分类随机森林算法。拟自适应分类随机森林算法综合了Adaboost算法和随机森林算法的优势,实验数据分析表明,训练集较大时,拟自适应随机森林算法的效果会好于随机森林算法。另外,拟自适应分类随机森林算法的收敛性确保它的推广误差可以通过训练集估计,所以,对于实际数据,拟自适应分类随机森林算法不需要把数据划分为训练集和测试集,从而,可以有效的利用数据信息。     

Ground Contact Detection with Shortstroke Buttons for Biped Robots

Robust walking motion of humanoid robots requires a sensor system that can accurately sense the robot's state and its environment. Especially in case the ground is not modeled and uneven, information about the contact state is crucial to initiate an appropriate response. Many humanoid robots use strain gauge-based force/torque sensors to obtain information about the contact state. In this paper, we propose the integration of shortstroke buttons in the foot design to detect ground contact faster and more reliably. Simulation results with our robot Lola suggest that impact forces in case of an unexpected ground contact can be reduced significantly by integrating these sensors. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Prediction Model for State Observation and Model Predictive Control of Biped Robots

Biped walking robots present a class of mechanical systems with many different challenges such as nonlinear multi-body dynamics, a large number of degrees of freedom and unilateral contacts. The latter impose constraints for physically feasible motions and in stabilization methods as the robot can only interact due to pressure forces with the environment. This limitation can cause the system to fall under unknown disturbances such as pushing or uneven terrain. In order to face such problems, an accurate and fast model of the robot to observe the current state and predict the state evolution into the future has to be used. This work presents a nonlinear prediction model with two passive degrees of freedom (dof), point masses and compliant unilateral contacts. We show that the model is applicable for real-time model predictive optimization of the robot's motion. Experiments on the biped robot LOLA [1] underline the effectiveness of the proposed model to increase the system's long term stability under large unknown disturbances. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Comparison of two algorithms for solving a two‐stage bilinear stochastic programming problem with quantile criterion

The paper is devoted to solving the two‐stage problem of stochastic programming with quantile criterion. It is assumed that the loss function is bilinear in random parameters and strategies, and the random vector has a normal distribution. Two algorithms are suggested to solve the problem, and they are compared. The first algorithm is based on the reduction of the original stochastic problem to a mixed integer linear programming problem. The second algorithm is based on the reduction of the problem to a sequence of convex programming problems. Performance characteristics of both the algorithms are illustrated by an example. A modification of both the algorithms is suggested to reduce the computing time. The new algorithm uses the solution obtained by the second algorithm as a starting point for the first algorithm. Copyright © 2015 John Wiley & Sons, Ltd.     

Chaos synchronization in noisy environment using nonlinear filtering and sliding mode control

This paper presents an algorithm for synchronizing two different chaotic systems, using a combination of the extended Kalman filter and the sliding mode controller. It is assumed that the drive chaotic system has a random excitation with a stochastically chaotic behavior. Two different cases are considered in this study. At first it is assumed that all state variables of the drive system are available, i.e. complete state measurement, and a sliding mode controller is designed for synchronization. For the second case, it is assumed that the output of the drive system does not contain the whole state variables of the drive system, and it is also affected by some random noise. By combination of extended Kalman filter and the sliding mode control, a synchronizing control law is proposed. As a case study, the presented algorithm is applied to the Lur’e-Genesio chaotic systems as the drive-response dynamic systems. Simulation results show the good performance of the algorithm in synchronizing the chaotic systems in presence of noisy environment.     

Dynamics-Based Motion Planning for a Pendulum-Actuated Spherical Rolling Robot

This paper deals with the dynamics and motion planning for a spherical rolling robot with a pendulum actuated by two motors. First, kinematic and dynamic models for the rolling robot are introduced. In general, not all feasible kinematic trajectories of the rolling carrier are dynamically realizable. A notable exception is when the contact trajectories on the sphere and on the plane are geodesic lines. Based on this consideration, a motion planning strategy for complete reconfiguration of the rolling robot is proposed. The strategy consists of two trivial movements and a nontrivial maneuver that is based on tracing multiple spherical triangles. To compute the sizes and the number of triangles, a reachability diagram is constructed. To define the control torques realizing the rest-to-rest motion along the geodesic lines, a geometric phase-based approach has been employed and tested under simulation. Compared with the minimum effort optimal control, the proposed technique is less computationally expensive while providing similar system performance, and thus it is more suitable for real-time applications.     

An approach to the modelling of worm‐like motion systems with finite degree of freedom – First steps in technical realization

The paper presents some investigations of worm‐like motion systems that have the earthworm as live prototype. The systems are modeled in form of straight chains of n interconnected mass points. The ground contact can be described by non‐symmetric dry friction. Analytical solutions using asymptotical methods are presented.     

Robot learning schemes that trade motion accuracy for command simplification

This study was inspired by the human motor control system in its ability to accommodate a wide variety of motions. By contrast, the biologically inspired robot learning controller usually encounters huge learning space problems in many practical applications. A hypothesis for the superiority of the human motor control system is that it may have simplified the motion command at the expense of motion accuracy. This tradeoff provides an insight into how fast and simple control can be achieved when a robot task does not demand high accuracy. Two motion command simplification schemes are proposed in this paper based on the equilibrium-point hypothesis for human motion control. Investigation into the tradeoff between motion accuracy and command simplification reported in this paper was conducted using robot manipulators to generate signatures. Signature generation involves fast handwriting, and handwriting is a human skill acquired via practice. Because humans learn how to sign their names after they learn how to write, in the second learning process, they somehow learn to trade motion accuracy for motion speed and command simplicity, since signatures are simplified forms of original handwriting. Experiments are reported that demonstrate the effectiveness of the proposed schemes.     

Application of a rule self-regulating fuzzy controller for robotic deburring on unknown contours

Most metal parts made by machining operations contain burrs, which can be removed by robotic manipulators. Modeling a deburring robot on unknown contours is a relatively difficult task. In this study, we present a novel compliant motion controller that uses a modified on-line rule self-regulating fuzzy control (RSFC) and depends on no mathematical models. In the proposed controller, a Cartesian robot on which a grinding tool is mounted rigidly performs edge following (precision deburring) and chamfering on unknown contours. The manipulator is controlled along the tangential direction of a constrained surface and its cutting force is maintained at a desired level. Experimental results demonstrate the effectiveness of this control strategy in terms of automatically deburring the edges of parts with an unknown geometrical configuration.     

Optimal Financial Portfolios

The classes of reward‐risk optimization problems that arise from different choices of reward and risk measures are considered. In certain examples the generic problem reduces to linear or quadratic programming problems. An algorithm based on a sequence of convex feasibility problems is given for the general quasi‐concave ratio problem. Reward‐risk ratios that are appropriate in particular for non‐normal assets return distributions and are not quasi‐concave are also considered.     

Analysis of unbonded contact problems by means of quadratic programming

Two-body, elastic, unbonded contact problems are formulated as quadratic programming problems. Uniqueness theorems of quadratic programming theory are applied to show that the solution of a contact problem, if one exists, is unique and can be readily found by the modified simplex method of quadratic programming. A solution technique that is compatible with finite-element methods is developed, so that contact problems with complex boundary configurations can be routinely solved. A number of classical and nonclassical problems are solved. Good agreement is found for problems with previously known solutions.